{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:36Z","timestamp":1761176256089,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Cognitive diagnosis (CD) aims to identify the extent to which learners have mastered different knowledge concepts, which is an essential task for online education platforms. Due to the increase in educational data and advancements in deep learning technologies, numerous CD models have been proposed, achieving promising results. However, most existing models lack a comprehensive understanding of learners\u2019 behavioral patterns, focusing primarily on global knowledge structures while overlooking fine-grained response behaviors. Therefore, we propose LAB-ICDM, an Inductive Cognitive Diagnosis Model Integrating the Characteristics of Learners\u2019 Answering Behavior. Specifically, (i) we introduce a global relation aggregation module, which constructs a learner-centered graph to capture global representations of learners; (ii) we design a relation-aware module, which leverages a learner-exercise graph to extract fine-grained behavioral patterns, enabling a more comprehensive representation of learners\u2019 exercise-solving behaviors and their interactions with exercises. By fusing these two modules, our model constructs a multi-view representation of learners\u2019 knowledge states, enabling more accurate cognitive diagnosis. Extensive experiments on three real-world educational datasets demonstrate that LAB-ICDM significantly outperforms existing state-of-the-art models, showcasing superior predictive performance.<\/jats:p>","DOI":"10.3233\/faia251264","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:22Z","timestamp":1761126982000},"source":"Crossref","is-referenced-by-count":0,"title":["Inductive Cognitive Diagnosis Model Integrating the Characteristics of Learners\u2019 Answering Behavior"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8884-1714","authenticated-orcid":false,"given":"Linhao","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7524-5999","authenticated-orcid":false,"given":"Sheng-hua","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4242-4840","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251264","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:22Z","timestamp":1761126982000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251264","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}